Boguslaw Twarog

The paper presents a new approach to optimising cluster structure by selecting servers to meet specified performance requirements while minimising costs. Modern applications are placing increasing demands on performance and are critical elements of business operations. As a result, the operation of such applications increasingly relies on server clusters. Selecting the type and number of servers is not a trivial task. The problem is further complicated by the widespread use of layered application architectures, which means that different hardware solutions may be optimal for handling different layers. The article proposes a technique that uses the Tabu Search heuristic in conjunction with a BCMP-based application model. An optimisation algorithm for the cluster structure is presented in two versions: minimising the solution cost while meeting performance requirements, and maximising performance while meeting budget constraints.

Non-intrusive monitoring of electrical loads (NILM) implemented by the state analysis method critically depends on the selection of appropriate features to identify devices. The commonly used expert selection is not optimal, and computational methods of feature selection require the establishment of an optimisation criterion that will ensure a satisfactory level of NILM system performance. An important element of the discussed method is the selection of the classifier and its matching with the selection method to construct a softsensor. In this work, four feature selection methods (Boruta, ReliefF, mRMR, the author's method) and four classifiers (decision trees, random forests, artificial neural networks and a hybrid classifier) were implemented and tested. Software was implemented for the softsensor architectures tested, enabling the verification of optimal configurations for NILM. The research confirmed that the selection of features using optimisation methods and the use of a softsensor allow for better support in the decision-making process.

This article presents an innovative approach to monitoring river water quality in real time by generating estimates of difficult-to-measure signals such as biochemical oxygen demand. Laboratory tests take too long for real-time monitoring. Therefore, an adaptive PDALM algorithm (Proportional Differential Algorithm with a Latch Mechanism) was developed, integrating mathematical modelling with measurement data to enable instantaneous estimation of water quality signals using a special latch mechanism. The forced eigenvalue distribution guarantees system dynamics and ensures stability and robustness to disturbances. In the proposed RTMS system, the PDALM algorithm functions as an adaptive soft sensor generating high-quality training data. This data is then used by a generative neural network for anomaly detection and forecasting of atypical scenarios in dynamic environmental systems. The system can function as an intelligent environmental monitoring module capable of learning, predicting, and responding to changing environmental conditions.

Eye-tracking technology has evolved into a cornerstone of modern behavioral and cognitive research, providing precise insight into how individuals perceive, interpret, and interact with their visual environments

The increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of information features and for the identification of operating electrical devices. A set of potential identification features was obtained from high-frequency measurements covering 12 types of electrical consumers and consisted of 218 features. From these, an identification vector was selected via the mRMR (minimum redundancy maximum relevance) method, which searches for features that are maximally correlated with the class and are as little correlated with each other as possible. Identification was realized by building a hybrid classifier using binary classifiers built from artificial neural networks and decision trees. The Accuracy, Precision, Recall, and F1 metrics were used to assess the quality of identification. The obtained values of the identification quality indicators confirm that it is possible to replace multiclass classification in NILM with binary classification without losing its quality. The use of binary classifiers allows for the identification of new devices without the need to change the classifier configuration.

This paper presents a vision system that measures the position of an aircraft relative to the runway (RWY) during a landing approach. It was assumed that all the information necessary for a correct approach was based entirely on an analysis of the image of the runway and its surroundings. It was assumed that the way the algorithm works, as well as possible, should imitate the pilot’s perception of the runway. Taking into account the above and the fact that the infrastructure at each airport is different, it has been decided to use artificial neural networks with a dedicated learning process for any airport, based on the simulation environments. Such an action will enable the generation of a synthetic video sequence without the need for costly and time-consuming flights. The presented solution was tested in real flight conditions on an experimental aircraft, and the selected test results are presented in this article.

The main objective of this research was to propose a smart technology to record and analyse the attention of operators of transportation devices where human–machine interaction occurs. Four simulators were used in this study: General Aviation (GA), Remotely Piloted Aircraft System (RPAS), AS 1600, and Czajka, in which a spatio-temporal trajectory of system operator attention describing the histogram distribution of cockpit instrument observations was sought. Detection of the position of individual instruments in the video stream recorded by the eyetracker was accomplished using a pre-trained Fast R-CNN deep neural network. The training set for the network was constructed using a modified Kanade–Lucas–Tomasi (KLT) algorithm, which was applied to optimise the labelling of the cockpit instruments of each simulator. A deep neural network allows for sustained instrument tracking in situations where classical algorithms stop their work due to introduced noise. A mechanism for the flexible selection of Area Of Interest (AOI) objects that can be tracked in the recorded video stream was used to analyse the recorded attention using a mobile eyetracker. The obtained data allow for further analysis of key skills in the education of operators of such systems. The use of deep neural networks as a detector for selected instrument types has made it possible to universalise the use of this technology for observer attention analysis when applied to a different objects-sets of monitoring and control instruments.

The waters of rivers are not only used for consumption, industry and agriculture but have also found their way into the transport and energy generation sectors. Many disturbances introduced into the aquatic environment are of the natural variety, which are the result of “admixtures” contained in water, e.g., through contact with soil, and of man-made types, which are directly related to humanities destructive influences. In the presented examinations, the most important processes affecting the spread and transport of these pollutants are taken into account, i.e., advection and diffusion. The authors present observations on the influence of the diffusion phenomenon on river flow modelling processes. Such an approach allows for the separation of the dynamics of water flow and the dynamics of transport of the dissolved substance mass. Specifically, phenomena occurring in relation to spatial coordinates, time and variable parameter values in the proposed mathematical model were analysed. Ultimately, this research will contribute to the correct design and implementation of a complementary diffusion module as an extension to an intelligent water quality control and monitoring system. The Intelligent Analytical Computing Control System architecture under development already includes other modules such as the Intelligent Filtration and Prediction Module and, complemented by the Intelligent Diffusion Module, provides a complementary tool for monitoring river hydromorphology. Implementation of the above solution will help to improve water quality, thus preventing and eliminating the appearance of undesirable pollutants in rivers, and increase the standard of living in the current threatened environmental world.